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Simplified Intestinal Microbiota to Study Microbe-Diet-Host Interactions in a MouseModel

Kovatcheva-Datchary, Petia; Shoaie, Saeed; Lee, Sunjae; Wahlström, Annika; Nookaew, Intawat; Hallen,Anna; Perkins, Rosie; Nielsen, Jens; Bäckhed, FredrikPublished in:Cell Reports

Link to article, DOI:10.1016/j.celrep.2019.02.090

Publication date:2019

Document VersionPublisher's PDF, also known as Version of record

Link back to DTU Orbit

Citation (APA):Kovatcheva-Datchary, P., Shoaie, S., Lee, S., Wahlström, A., Nookaew, I., Hallen, A., ... Bäckhed, F. (2019).Simplified Intestinal Microbiota to Study Microbe-Diet-Host Interactions in a Mouse Model. Cell Reports, 26(13),3772-3783. https://doi.org/10.1016/j.celrep.2019.02.090

Resource

Simplified Intestinal Micro

biota to Study Microbe-Diet-Host Interactions in a Mouse Model

Graphical Abstract

Highlights

d Mice are colonized with ten bacterial strains to create a

simple human microbiota model

d Dietary changes alter colonization patterns of the simplified

intestinal microbiota (SIM)

d SIM-diet interactions affect some circulating metabolites in

the host

d The SIM affects host metabolism in a diet-specific manner

Kovatcheva-Datchary et al., 2019, Cell Reports 26, 3772–3783March 26, 2019 Crown Copyright ª 2019https://doi.org/10.1016/j.celrep.2019.02.090

Authors

Petia Kovatcheva-Datchary,

Saeed Shoaie, Sunjae Lee, ...,

Rosie Perkins, Jens Nielsen,

Fredrik Backhed

Correspondencefredrik.backhed@wlab.gu.se

In Brief

Kovatcheva-Datchary et al. develop a

mouse model colonized with a simplified

intestinal microbiota (SIM) to investigate

the microbe-microbe and host-microbe

interactions in the mammalian gut,

focusing on host metabolism. They

combine dietary interventions and

different omics approaches to show the

potential of the SIM model to study the

microbe-diet-host interplay.

Cell Reports

Resource

Simplified Intestinal Microbiota to StudyMicrobe-Diet-Host Interactions in a Mouse ModelPetia Kovatcheva-Datchary,1,6,8 Saeed Shoaie,2,8 Sunjae Lee,2 Annika Wahlstrom,1 Intawat Nookaew,3,7 Anna Hallen,1

Rosie Perkins,1 Jens Nielsen,3,4 and Fredrik Backhed1,5,9,*1Wallenberg Laboratory, Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, 41345, Sweden2Centre for Host–Microbiome Interactions, Dental Institute, King’s College London, SE1 9RT, UK3Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, 41345, Sweden4Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark5Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology,

Faculty of Health Sciences, University of Copenhagen, Copenhagen, 2200, Denmark6Present address: CAS Key Laboratory of Separation Science for Analytical Chemistry, Scientific Research Center for Translational Medicine,Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023 China7Present address: Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock,

AR 72205, USA8These authors contributed equally9Lead Contact

*Correspondence: fredrik.backhed@wlab.gu.se

https://doi.org/10.1016/j.celrep.2019.02.090

SUMMARY

The gut microbiota canmodulate humanmetabolismthrough interactions with macronutrients. However,microbiota-diet-host interactions are difficult tostudy because bacteria interact in complex foodwebs in concert with the host, and many of thebacteria are not yet characterized. To reduce thecomplexity, we colonize mice with a simplified intes-tinal microbiota (SIM) composed of ten sequencedstrains isolated from the human gut with comple-menting pathways to metabolize dietary fibers. Wefeed the SIM mice one of three diets (chow [fiberrich], high-fat/high-sucrose, or zero-fat/high-sucrosediets [both low in fiber]) and investigate (1) howdietary fiber, saturated fat, and sucrose affect theabundance and transcriptome of the SIM commu-nity, (2) the effect of microbe-diet interactions oncirculating metabolites, and (3) how microbiota-dietinteractions affect host metabolism. Our SIM modelcan be used in future studies to help clarify howmicrobiota-diet interactions contribute to metabolicdiseases.

INTRODUCTION

The human gut is populated with a dense and complex com-

munity of microbes, collectively known as the gut microbiota

(Dethlefsen et al., 2007). The dominating phyla in the human

gut are Firmicutes, Bacteroidetes, Actinobacteria, Proteobacte-

ria, and Verrucomicrobia, but other phyla, such as Fusobacte-

ria, Cyanobacteria, Lentisphaerae, Spirochaetes, and TM7, are

also present (Arumugam et al., 2011; Qin et al., 2010). The gut

microbiota is associated with many essential functions for host

3772 Cell Reports 26, 3772–3783, March 26, 2019 Crown Copyright ªThis is an open access article under the CC BY license (http://creative

physiology (Sommer and Backhed, 2013). For example, a key

function of microbes in the gut is to process complex carbohy-

drates that cannot be digested by host enzymes (Sonnenburg

and Backhed, 2016) into short-chain fatty acids (SCFAs), pri-

marily acetate, propionate, and butyrate, and organic acids

such as succinate and lactate (Cummings and Macfarlane,

1991; Koh et al., 2016; Macfarlane and Macfarlane, 2012).

These metabolites are not only essential for the growth and

cellular function of certain microbes in the gut (Cummings

and Macfarlane, 1991; Fischbach and Sonnenburg, 2011; Koh

et al., 2016) but also affect host physiology (Koh et al., 2016;

Zhao et al., 2018).

Although the production of SCFAs resulting from the bacterial

fermentation of fiber-rich diets is generally associatedwith bene-

ficial metabolic effects, the increased energy harvest has also

been proposed to contribute to diet-induced obesity in mice

(Koh et al., 2016). Furthermore, the gut microbiota produces

other metabolites that are influenced by the diet, many of which

are likely to play a role in host physiology (Koh et al., 2016; Makki

et al., 2018; Zmora et al., 2019). Studies to delineate the interac-

tions between gut bacteria, diet, and host metabolism are chal-

lenging because (1) of the complexity and high inter-individual

variability of human gut microbiota, (2) bacteria interact in com-

plex food webs in concert with the host, and (3) the microbiota

consists mainly of non-sequenced members, thus limiting inter-

pretation from metagenomic and metatranscriptomic analyses.

One approach to overcome these issues is to use gnotobiotic

animals colonized with a simplified intestinal microbial commu-

nity consisting of well-characterized bacteria from humans. A

recent example of such a model used rats colonized with eight

bacterial species from the human gut to investigate how the mi-

crobiota composition changes in response to dietary challenges

(Becker et al., 2011). Others have used mice colonized with a

defined community of human bacteria to investigate microbe-

microbe interactions (McNulty et al., 2013; Rey et al., 2013) or

the interactions between microbiota, dietary fiber, and the

2019commons.org/licenses/by/4.0/).

Table 1. Phylogenetic and Metabolic Features of the Members of the SIM

SIM Bacterium Phylum Metabolic Function Produced Metabolites References

Akkermansia muciniphila:

DSM 22959

Verrucomicrobia mucin degradation acetate, propionate Derrien et al. (2004)

Bacteroides thetaiotaomicron:

ATCC 29148

Bacteroidetes polysaccharide

breakdown; mucin

acetate, propionate,

succinate

Martens et al. (2008);

Xu et al. (2003)

Bifidobacterium adolescentis:

L2-32

Actinobacteria di- and oligosaccharide

breakdown

acetate, lactate Duranti et al. (2016);

Marquet et al. (2009);

Pokusaeva et al. (2011)

Collinsella aerofaciens:

DSM 3979

Actinobacteria di- and oligosaccharide

breakdown

acetate, lactate,

formate, H2

Flint et al. (2012);

Kageyama and Benno (2000)

Desulfovibrio piger:

DSM 749

Proteobacteria sulfate reducer,

lactate user

acetate, H2S Marquet et al. (2009)

Eubacterium hallii: L2-7 Firmicutes di- and monosaccharide

breakdown; lactate user

butyrate Duncan et al. (2004b);

Scott et al. (2014)

Eubacterium rectale: A1-86 Firmicutes oligosaccharide

breakdown; acetate

user

butyrate, lactate,

formate, H2

Duncan and Flint (2008);

Duncan et al. (2008)

Prevotella copri:

DSM 18205

Bacteroidetes polysaccharide

breakdown

succinate, H2 Flint et al. (2012);

Hayashi et al. (2007);

Kovatcheva-Datchary et al. (2015)

Roseburia inulinivorans:

A2-194

Firmicutes oligosaccharide

breakdown

butyrate, propionate Duncan et al. (2006);

Scott et al. (2006)

Ruminococcus bromii:

L2-63

Firmicutes polysaccharide

breakdown

acetate, formate, H2 Crost et al. (2018);

Ze et al. (2012)

colonic mucus barrier (Desai et al., 2016). However, none of

these studies investigated how diet-induced changes in the

gut microbiota affect host metabolism in parallel with altered

microbial gene expression.

To investigate the effect of microbiota-diet interactions on

host metabolism, we developed a gnotobiotic mouse model

colonized with ten representatives of the human intestinal mi-

crobiota (simplified intestinal microbiota [SIM]). Our selection

was based on the following criteria: (1) All members of the

dominant phyla in the human gut should be represented. (2)

The chosen strains must have been sequenced, thus allowing

us to characterize how specific nutrients (fiber, saturated fat,

and sucrose) affect the microbial gene expression. (3) The

chosen strains must have well-characterized metabolic func-

tions (to digest and/or ferment carbohydrates) and trophic

interactions with a range of cross-feeding interactions for die-

tary conversions in the gut. The main metabolic function and

metabolites produced by each SIM bacterial strain in in vitro

studies are presented in Table 1. Because H2 is produced dur-

ing fermentation and accumulation of H2 decreases the meta-

bolic activity of microbes (Rey et al., 2013), we also included

the H2-consuming sulfate-reducing bacterium Desulfovibrio

piger.

RESULTS AND DISCUSSION

Colonization Pattern of the SIM Community in Mice on aChow DietWe introduced the ten selected bacteria (Table 1) into germ-free

(GF) Swiss Webster mice and maintained them for four genera-

tions to generate stably colonized SIM mice. We showed that

all ten bacteria of the SIM community colonized each region of

the gut of SIM mice on a chow diet, although Eubacterium hallii

was present only at a low density throughout (Figure 1A). Levels

of SIM bacteria were higher in the distal part of the gut compared

with the small intestine, with the highest levels in the feces of SIM

mice (Figure 1A), consistent with the fact that the SIM bacteria

were chosen because of their importance for carbohydrate

fermentation.

A previous metagenome analysis has shown that all of the

SIM bacteria, with the exception of Roseburia inulinivorans,

are frequent colonizers of the human gut (Qin et al., 2010).

We confirmed that the ten taxa of the SIM community were pre-

sent in feces from two healthy human donors (Figures 1B and

1C). For comparison with SIM mice, we then colonized GF

mice with unfractionated microbiota from each of the two hu-

man donors for 2 weeks. We showed that all ten SIM bacteria

were present in the cecum of chow-fed mice colonized with

feces from one of these donors (Figure 1B) and all except

Collinsella aerofaciens were present in the cecum of chow-

fed mice colonized with microbiota from the second donor (Fig-

ure 1C). This unsuccessful colonization of C. aerofaciens may

be explained by the fact that the second donor had low fecal

levels of C. aerofaciens and that this species must compete

with other members of the human microbiota beyond the ten

present in the SIM community. However, the overall profiles

of the ten SIM bacteria were similar in the SIM mice and the

humanized mice on a chow diet, indicating that the SIM com-

munity may be a suitable simplified model of the human gut

microbiota.

Cell Reports 26, 3772–3783, March 26, 2019 3773

A

B C

Figure 1. SIM Bacteria Colonize the Mouse Gut

(A) Abundance of each of the SIM bacteria in jejunum, ileum, cecum, colon, and feces of chow-fed SIM male mice (n = 5; mice are from two independent

experiments; each sample was analyzed in duplicate in one run and in duplicate PCR runs).

(B) Abundance of each of the SIM bacteria in the feces of a female human donor and in the cecum of chow-fed Swiss Webster female mice (n = 6; each sample

was analyzed in duplicate in one run and in duplicate PCR runs) colonized with feces from this donor.

(C) Abundance of each of the SIM bacteria in the feces of a male human donor and in the cecum of chow-fed Swiss Webster male mice (n = 5; each sample was

analyzed in duplicate in one run and in duplicate PCR runs) colonized with feces from this donor.

Data are mean ± SEM.

Dietary Changes Modify the Colonization Pattern of theCecal SIM CommunityA major selection criterion of the SIM bacteria was their capacity

to metabolize carbohydrates and participate in anaerobic food

webs in the gut. We therefore assessed how the abundance of

SIM members was affected by changing the mouse diet from

chow, which is low in fat (9% by weight) and high in dietary fiber

(i.e., plant polysaccharides; 15% by weight), to one of two puri-

fied diets with low dietary fiber (primarily cellulose) but different

macronutrient compositions. One group of SIM mice received

a ‘‘Western’’ high-fat/high-sucrose (HF-HS) diet (fat 20% by

weight, sucrose 18% by weight) for 2 weeks; a second group

received a zero-fat/high-sucrose (ZF-HS) diet (sucrose 63% by

weight) for 2 weeks (Table S1); a third group remained on chow.

The total bacterial load in the cecum of SIM mice was not

affected by a change in the diet (Figure 2A). However, compared

with SIMmice that remained on chow, SIMmice that switched to

a diet low in dietary fiber had reduced cecal abundance of Pre-

votella copri and Bifidobacterium adolescentis (with either

HF-HS or ZF-HS diet), Ruminococcus bromii (with HF-HS diet),

and R. inulinivorans (with ZF-HS diet) as well as a trend toward

reduced abundance of C. aerofaciens (with ZF-HS diet; p =

0.052) (Figure 2A). Thus, a reduction in dietary fiber reduced

3774 Cell Reports 26, 3772–3783, March 26, 2019

the abundance of these fiber-degrading bacteria. These results

are consistent with earlier studies showing that P. copri,

B. adolescentis, R. inulinivorans, R. bromii, and C. aerofaciens

have increased abundance when the diet is rich in complex plant

polysaccharides (Kovatcheva-Datchary et al., 2015; Ramirez-

Farias et al., 2009; Scott et al., 2014; Walker et al., 2011; Vital

et al., 2018). Furthermore, a recent study reported that bacteria

from the phylogenetic order Bacteroidales (to which Prevotella

species belong) are lost in mice after several generations of

feeding with a Western diet (Sonnenburg et al., 2016). Similarly,

a loss of Prevotella strains has been reported in the gut micro-

biome of non-Western individuals after immigration to aWestern

country (Vangay et al., 2018). In addition, two recent studies in

humans showed that short-term dietary changes to reduce com-

plex carbohydrates resulted in rapid reductions in abundance of

Bifidobacterium, Ruminococcus, and Roseburia, bacteria that

metabolize complex carbohydrates in the gut (David et al.,

2014; Mardinoglu et al., 2018).

The cecal abundance of E. hallii was higher in SIM mice that

switched from chow to an HF-HS or a ZF-HS diet (Figure 2A).

In agreement, a dietary intervention study in humans showed

that resistant starch (a prominent dietary fiber) reduced the

abundance of E. hallii (Salonen et al., 2014). Furthermore,

B

A

C

Figure 2. Dietary Changes Affect the Cecal SIM Community

(A) Abundance of each of the SIM bacteria in the cecum of SIM mice that

remained on chow or switched to an HF-HS diet or a ZF-HS diet for 2 weeks

(n = 8–11; mice are from two independent experiments; each sample was

analyzed in duplicate in one run and in duplicate PCR runs).

(B) Concentrations of SCFAs and organic acids in the cecum of GF mice (n = 5

or 6) and SIM mice that remained on chow or switched to an HF-HS diet or a

ZF-HS diet for 2 weeks (n = 8–11; mice are from two independent experi-

ments). Metabolite concentrations for GF mice are shown by hashed lines and

overlay data for SIM mice. Data are mean ± SEM. *p < 0.05, **p < 0.01, and

***p < 0.001 versus chow (one-way ANOVA).

(C) Positive (red) and negative (blue) fold changes in cecal expression of genes

for each of the SIM bacteria in mice that switched to a ZF-HS diet (outer circle)

or an HF-HS diet (inner circle) compared with mice that remained on chow for

2 weeks (n = 5; mice are from two independent experiments). Inner circle,

genome coverage of generated RNA-seq data.

See also Table S2.

in vitro studies have shown that E. hallii can use a broad range of

substrates, including glucose, lactose, galactose, glycerol, and

amino sugars (Bunesova et al., 2018; Duncan et al., 2004a; En-

gels et al., 2016; Scott et al., 2014). We observed a non-signifi-

cant increase in the abundance of Akkermansia muciniphila

and no change in the abundance of Bacteroides thetaiotaomi-

cron or Eubacterium rectale in SIM mice that switched from

chow to an HF-HS or a ZF-HS diet (Figure 2A), consistent with

the fact that these bacteria are not exclusively dependent on di-

etary fiber and can degrade hostmucin or simple sugars from the

diet. A. muciniphila is an avid user of mucin glycans (Derrien

et al., 2004). B. thetaiotaomicron is able to digest a broad range

of dietary polysaccharides and adapts to different sources of

carbohydrates, including mucin glycans, in the absence of die-

tary fibers (Sonnenburg et al., 2005). E. rectale can metabolize

different carbohydrates (amylopectin, amylose, arabinoxylan,

fructo-oligosaccharide, galacto-oligosaccharide, inulin, xylo-

oligosaccharide) (Sheridan et al., 2016) but has also been shown

to grow well on glucose (Cockburn et al., 2015; Ze et al., 2012).

The abundance of D. piger was also not affected by a change

in diet (Figure 2A). The growth and survival of D. piger is depen-

dent on the availability of sulfate (Rey et al., 2013), which in the

mammalian gut is sourced not only from the diet but also from

sulfated glycans in host mucin. Although D. piger lacks sulfatase

activity, A. muciniphila and B. thetaiotaomicron are capable of

removing sulfate from sulfated glycans when metabolizing host

glycans and thus can contribute to the availability of sulfate

required by D. piger (Rey et al., 2013).

There were no significant differences in individual bacterial

species between mice on an HF-HS or a ZF-HS diet (Figure 2A),

indicating that a reduction in plant polysaccharides has a greater

influence on the abundance of the SIM members than variations

in either fat or sucrose.

Dietary Changes Affect the Fermentative Capacity ofthe Cecal SIM CommunityWe confirmed the functional capacity of the SIM community to

metabolize carbohydrates by showing that levels of SCFAs

and organic acids were dramatically higher in cecal samples

from SIM mice compared with GF mice on a chow diet (Fig-

ure 2B). To investigate the functional response of the SIMmicro-

biota to dietary changes, we also compared SCFA levels in cecal

samples from SIM mice that switched to an HF-HS or a ZF-HS

diet with those that remained on chow. We showed that a switch

to a diet low in plant polysaccharides resulted in reduced cecal

concentrations of the SCFAs acetate and propionate as well as

of the organic acids lactate and succinate (Figure 2B), in parallel

with the reduced abundance of P. copri, B. adolescentis,

R. bromii, and R. inulinivorans observed in response to this diet

change (Figure 2A).

Butyrate was not significantly affected when SIM mice

switched to a ZF-HS or an HF-HS diet (Figure 2B), consistent

with the unchanged/increased abundance of the known butyrate

producers E. rectale and E. hallii in response to these diets

change (Figure 2A). Previous in vitro findings have shown that

E. hallii can produce butyrate from glucose, lactate, acetate,

and mucin-derived monosaccharides (Bunesova et al., 2018;

Duncan et al., 2004b; Scott et al., 2014; Shetty et al., 2017).

Cell Reports 26, 3772–3783, March 26, 2019 3775

Dietary Changes Modify the Transcriptome of the CecalSIM CommunityTo further investigate the functional response of the SIM commu-

nity to dietary changes, we performed RNA sequencing on cecal

microbiota from the SIM mice that remained on chow or

switched to an HF-HS or a ZF-HS diet for 2 weeks. We obtained

an average depth of 15 million paired-end reads per sample,

which was sufficient to cover the genomes of each SIM bacte-

rium (Figure 2C). We identified differences (adjusted p < 0.05)

in 5,765 and 3,134 genes in cecal samples from the mice that

switched to an HF-HS and a ZF-HS diet, respectively, compared

with SIM mice that remained on chow (Table S2). By contrast,

only 166 genes were significantly differently expressed when

comparing samples from the mice on an HF-HS versus a

ZF-HS diet (Table S2). These results demonstrate that gene

expression in the SIM cecal microbiota is influenced to a greater

extent by a reduction in plant polysaccharides than variations in

either fat or sucrose.

We compared differences in gene expression at the species

level in SIM mice fed an HF-HS or a ZF-HS diet with SIM mice

fed chow and observed that (1) E. hallii, A. muciniphila,

B. thetaiotaomicron, andD. piger hadmostly upregulated genes,

consistent with increased or no change in abundance of these

species in the absence of plant polysaccharides, and (2)

P. copri had exclusively downregulated genes, consistent with

its reduced abundance in the absence of fiber (Figure 2C). In

addition, R. inulinivorans, R. bromii, B. adolescentis, E. rectale,

and C. aerofaciens had both upregulated and downregulated

genes (Figure 2C), consistent with the fact that these SIM bacte-

ria are capable of engaging in different trophic interactions (e.g.,

by competition and cooperation) to meet their nutrient require-

ments. These species can be primary degraders of dietary car-

bohydrates (Flint et al., 2012; Pokusaeva et al., 2011; Scott

et al., 2011; Sheridan et al., 2016; Ze et al., 2012) and are also

involved in metabolic cross-feeding interactions to maintain

gut homeostasis (Belenguer et al., 2006; Crost et al., 2018; Flint

et al., 2015; Marquet et al., 2009).

To explore functional features, we next used KEGG orthol-

ogy (KO) and the protein domain database (Pfam) to annotate

genes that were significantly altered in cecal samples from

mice fed HF-HS or ZF-HS compared with mice fed chow for

each member of the SIM microbiota (Table S2). A switch to

reduced plant polysaccharides in the diet resulted in (1)

decreases in KOs associated with the fermentation of plant

polysaccharides (e.g., alpha-amylase, beta-glucosidase, en-

doglucanase, and xylan 1,4-beta-xylosidase), specifically in

P. copri, E. rectale, and B. adolescentis; (2) increases in KOs

associated with the conversion of simple sugars (such as

ribose, ribulose, xylulose, fructose, mannose, sucrose, and

glucose), amino sugars, pyruvate, and SCFAs (mostly buty-

rate and propionate), particularly in B. thetaiotaomicron,

A. muciniphila, E. hallii, and R. inulinivorans; and (3) increases

in KOs associated with the metabolism of mucin, particularly

in A. muciniphila related to the degradation of mucin

O-glycans and in E. hallii related to the metabolism of mucin

monosaccharides (primarily galactose, glucose, and N-acetyl-

glucosamine) (Table S2). For most of the genes that were

significantly altered in SIM mice that switched to reduced

3776 Cell Reports 26, 3772–3783, March 26, 2019

plant polysaccharides in the diet, we obtained similar annota-

tions with both Pfam domains and KOs (Table S2). The most

frequent Pfam annotations in genes that were significantly

altered included those that were related to amino acid meta-

bolism and to the transport of degraded carbohydrates

(Table S2).

Dietary Changes Affect Carbohydrate-Active EnzymeGenes in the Cecal SIM CommunityTo investigate how dietary changes affect the contribution of the

individual bacteria to the conversion of dietary carbohydrates,

we next analyzed changes in SIM transcripts encoding carbohy-

drate-active enzymes (CAZymes) in cecal samples frommice fed

an HF-HS or a ZF-HS diet compared with mice fed chow using

the meta server dbCAN2, which integrates three tools for

CAZyme genome annotation (Zhang et al., 2018). We observed

similar results with all three tools (Table S3). In total, we identified

diet-induced alterations in genes encoding 192 uniqueCAZymes

belonging to 24 different families, including (1) enzymes involved

in the assembly of carbohydrates such as glycosyl transferases

(Lairson et al., 2008); (2) enzymes involved in carbohydrate

breakdown including auxiliary activity enzymes, carbohydrate

esterases, glycoside hydrolases, and polysaccharide lyases;

and (3) accessory modules such as carbohydrate-binding mod-

ules (El Kaoutari et al., 2013) (Table S3).

A subset of these CAZymes showed the same pattern of

expression changes in SIM mice that switched to either an

HF-HS or a ZF-HS diet compared with SIM mice that remained

on chow (Figure 3). For example, we observed reduced gene

expression of P. copri-affiliated CAZymes potentially involved

in the conversion of plant polysaccharides (including cellulose,

hemicellulose, beta-glucans, beta-mannan, pectin, and starch)

in mice that switched to either of the fiber-reduced diets (Fig-

ure 3), in parallel with the reduced abundance of P. copri in these

mice (Figure 2A) and consistent with studies showing that Prevo-

tella abundance is associated with fiber intake in humans (David

et al., 2014; Wu et al., 2011). In addition, we observed increased

gene expression ofA.muciniphila-affiliated CAZymes potentially

involved in the metabolism of host glycans and mucin in mice

that switched to either of the fiber-reduced diets (Figure 3).

A recent study in mice colonized with a synthetic human gut

microbiota reported enhanced mucin degradation in response

to a fiber-deprived diet (Desai et al., 2016). In this earlier

study, A. muciniphila and Bacteroides caccae but not

B. thetaiotaomicron contributed to the degradation of host gly-

cans and mucin. However, it is likely that B. thetaiotaomicron

is outcompeted by B. caccae if both are present; B. caccae

had a higher colonization rate inmice colonizedwith selected hu-

man bacteria and fed a fiber-free diet (Desai et al., 2016) and has

earlier been shown to have a higher growth rate on mucin and

host glycans in vitro (Desai et al., 2016; McNulty et al., 2013).

A number of the diet-induced changes in CAZyme gene

expression were specific to the dietary change (Figure 3). For

example, in mice that switched to an HF-HS diet, we observed

increased gene expression of CAZymes associated with conver-

sion of plant polysaccharides, degradation of mucin, and host

glycans, and synthesis of lipopolysaccharide and affiliated with

E. hallii, A. muciniphila, R. inulinivorans, and B. adolescentis

Figure 3. Dietary Changes Affect CAZyme

Expression in the Cecal SIM Community

Log fold changes in transcripts encoding

CAZymes in the metatranscriptomics data of

cecal samples from SIMmice fed HF-HS or ZF-HS

compared with SIM mice fed chow (n = 5 mice/

group; mice are from two independent experi-

ments). Only the CAZyme families with adjusted

p values < 0.05 are shown as averages. We clas-

sified CAZyme families on the basis of respective

substrates; GH5 can potentially convert beta-

mannan in addition to cellulose, hemicellulose,

and beta-glucans. AA, auxiliary activities; CBM,

carbohydrate-binding module; CE, carbohydrate

esterase; GH, glycoside hydrolase; GT, glycosyl

transferase; PL, polysaccharide lyase.

See also Table S3.

(Figure 3). In contrast, only a few transcripts were increased spe-

cifically in mice that switched to a ZF-HS diet (Figure 3); these

transcripts mostly belong to glycosyl transferase families, which

are involved in carbohydrate assembly rather than degradation

(Lairson et al., 2008). Furthermore, we observed reduced gene

expression of E. rectale-affiliated CAZymes associatedwith con-

version of plant polysaccharides mainly in SIM mice that

switched to a ZF-HS diet (glycoside hydrolase 94 was the only

E. rectale-affiliated CAZyme that showed reduced gene expres-

sionwhen SIMmice switched to either an HF-HS or a ZF-HS diet)

(Figure 3).

Together these findings show the flexibility of the SIM commu-

nity to efficiently metabolize the carbohydrates from the diet.

They also indicate that the diet-induced reduction in the fermen-

Cell Rep

tative capacity of the SIM community is

not a result of high levels of fat in the

diet but rather is caused by the reduction

in carbohydrates that can bemetabolized

by the bacteria.

Dietary Changes Affect SIM-Produced Metabolites in thePortal VeinTo investigate the effect of microbe-diet

interactions on circulating metabolites,

we performed metabolomics on plasma

samples collected from the portal vein

of the SIM mice that switched to an HF-

HS or a ZF-HS diet for 2 weeks and

from those that remained on chow. To

discriminate between host and micro-

bially derived plasma metabolites, we

also analyzed plasma samples from GF

mice on the three different diets. In total,

548 metabolites were measured (Table

S4); of these, metabolites whose levels

were significantly altered by the micro-

biota (75) or by diet change (89) are

shown in Figures 4A and 4B, respectively.

We identified 17 metabolites that were

both microbially regulated and modulated by dietary changes

in plasma samples from SIM mice (shown in red text in Figures

4A and 4B). These metabolites were mostly linked to lipid and

amino acid metabolism.

Specifically, the 17 microbially regulated metabolites that

were affected by reducing the consumption of dietary fiber

were (1) the unsaturated fatty acids oleate and 10-nanodece-

noate (increased in SIM mice on HF-HS or ZF-HS versus

chow); (2) the acylcarnitines 2-methylbutyrylcarnitine, 2-methyl-

butyrylglycine, isobutyrylcarnitine, propionylcarnitine, and pro-

pionylglycine; the purine metabolite xanthosine; succinate; and

ferulic acid 4-sulfate (decreased in SIM mice on HF-HS or

ZF-HS versus chow); and (3) kynurenate, anthranilate, and

xanthurenate, key metabolites in the kynurenine pathway of

orts 26, 3772–3783, March 26, 2019 3777

A B

(legend on next page)

3778 Cell Reports 26, 3772–3783, March 26, 2019

tryptophan catabolism (decreased in SIMmice on HF-HS versus

chow and increased in SIM mice on ZF-HS versus chow) (Fig-

ure 4B; Table S4).

The reduction of succinate in the portal vein of SIMmice on an

HF-HS or a ZF-HS diet versus chowwas consistent with reduced

cecal levels of succinate (Figure 2B). P. copri is an efficient suc-

cinate producer (Franke and Deppenmeier, 2018; Kovatcheva-

Datchary et al., 2015), and we have previously demonstrated

that succinate may improve metabolism by stimulating intestinal

gluconeogenesis (De Vadder et al., 2016). Ferulic acid 4-sulfate,

which was also reduced in the portal vein of SIM mice on an

HF-HS or a ZF-HS diet versus chow, is obtained after conjuga-

tion of ferulic acid during its uptake in the intestinal epithelium

and passage through the liver (Bourne and Rice-Evans, 1998).

The beneficial effect of ferulic acid and ferulic acid 4-sulfate in

diabetes, cardiovascular diseases, improved lipid metabolism,

and blood pressure lowering has been well recognized (Alam

et al., 2016; Van Rymenant et al., 2017; Vinayagam et al.,

2016). Thus, dietary fiber-induced increases in succinate and

ferulic acid 4-sulfate may be beneficial for host metabolism.

To investigate if we could identify links between any of these

17 portal vein metabolites (which were both microbially regu-

lated and modulated by dietary changes) and SIM-regulated

genes, we searched for SIM- and diet-regulated operons in the

metatranscriptome and investigated whether their resulting

genes could contribute to SIM- and diet-induced regulation

of the portal vein metabolites. We identified one operon (of

E. rectale consisting of EUBREC_1041 and EUBREC_1042)

that was significantly reduced in the cecal transcriptome

of SIM mice on a ZF-HS diet versus chow (Table S2).

EUBREC_1041 encodes acyl-CoA thioesterase (Mahowald

et al., 2009) and EUBREC_1042 encodes arabinofuranosidase

(also termed xylan 1,4-beta-xylosidase), which can release

ferulic acid from dietary fibers (Duncan et al., 2016; Nishizawa

et al., 1998). We next searched the CAZyme dataset for enzyme

families that could contribute to microbiota activity associated

with ferulic acid release. We observed that transcripts of the

carbohydrate esterase family CE1, which includes ferulic acid

esterase activity (Dodd and Cann, 2009), were decreased in

cecal samples from mice on a ZF-HS diet versus chow, with a

major contribution of E. rectale (Figure 3). Thus, we propose

that E. rectale, which is abundant in the human microbiome,

may contribute to ferulic acid release from diets rich in plant

polysaccharides in the mammalian gut.

SIM Affects Host Metabolism in a Diet-Specific FashionTo investigate whether the SIM system also could be used to

study microbe-diet effects on host metabolism, we compared

body weight, adiposity, steatosis, and glucose metabolism in

SIM versus GF mice on each of the three diets.

Figure 4. Dietary Changes and Microbiota Affect Plasma Metabolites

(A) Heatmap showing statistically significant fold changes in concentrations of m

independent experiments) versus GF mice (n = 6–8) on chow, HF-HS, and ZF-H

(B) Heatmap showing statistically significant fold changes in concentrations of m

two independent experiments) or ZF-HS (n = 5; mice are from two independent ex

experiments) (false discovery rate [FDR], q < 0.1). Metabolites significantly regul

See also Table S4.

In mice on chow, we observed that SIM was sufficient to in-

crease body weight and epididymal adipose weight, induce

hepatic steatosis, and increase blood glucose levels (Figures

5A–5E). Thus, SIM has effects on host metabolism similar to

those seen in mice colonized with a normal mouse microbiota

(Backhed et al., 2004; Caesar et al., 2012) and may induce

adiposity, resulting in impaired glucose tolerance by enhancing

energy harvest (i.e., increased SCFA production from dietary

fibers) in the host.

In mice on an HF-HS diet, we again demonstrated that SIM

increased blood glucose levels (Figure 5E). However, this

response was not paralleled by an increase in body weight (Fig-

ure 5A), in contrast to our earlier study comparing HF-HS diet-

fed conventionally raised and GF mice (Backhed et al., 2007).

This discrepancy may be explained by the fact that the present

diet intervention was shorter than in the previous study (2 versus

8weeks) and that anothermouse strainwas used (SwissWebster

versus C57BL/6). However, we observed increased adiposity

and steatosis in SIM versus GF mice on an HF-HS diet (Figures

5B–5D), and both of these responses are known to be associated

with impaired glucose tolerance (Cani et al., 2007).

In mice on the most extreme diet, ZF-HS, we observed that

SIM had its main effect on hepatic steatosis, resulting in a 2.8 ±

0.5 fold increase in liver fat compared with GF mice on the

same diet (Figure 5D). SIM promoted a small increase in body

weight and epididymal white adipose weight (Figures 5A and

5C), without affecting overall adiposity determined by magnetic

resonance imaging (Figure 5B) in mice on a ZF-HS diet. Surpris-

ingly, we did not observe significant changes in blood glucose

levels in SIMmice comparedwithGFmice on theZF-HSdiet (Fig-

ure 5E), despite the difference in hepatic steatosis. One explana-

tion can be that the ZF-HS diet has direct effects on glucose

metabolism in GF mice, independently of steatosis, as the

glucose excursion curve was elevated in GF mice on this diet

compared with GF mice fed chow or an HF-HS diet (Figure 5E).

ProspectusStudies of the gut microbiota are complex and often limited by a

lack of sequenced strains and poor gene annotations. To over-

come these limitations, we colonized GF mice with a set of rela-

tively well-characterized bacteria that represent the main phyla

in the human gut and used the model to investigate how SIM-

diet interactions affect the transcriptome, metabolome, and

host metabolism. The SIM community has many of the features

of the complete microbiota in modulating host metabolism in a

diet-dependent fashion and, as such, the SIM model may

facilitate our understanding of how the microbes interact with

macronutrients to affect host metabolism. Because we use bac-

terial strains isolated from humans, our findings may be extrap-

olated to humans and potentially provide guidance for human

etabolites in portal vein plasma from SIM mice (n = 5–7; mice are from two

S diets.

etabolites in portal vein plasma from SIM mice on HF-HS (n = 7; mice are from

periments) diet versus SIMmice on chow (n = 7; mice are from two independent

ated in both datasets (A and B) are listed in red.

Cell Reports 26, 3772–3783, March 26, 2019 3779

A B

C D

E

Figure 5. Metabolic Phenotypes of the SIM

Mice in Response to Diet

(A–E) Body weight (A), body fat (B), epididymal fat

(C), liver fat (D), and blood glucose (E) of SIM mice

(n = 5) compared with GFmice (n = 5 or 6) on chow,

HF-HS, and ZF-HS diets. Data are mean ± SEM.

*p < 0.05, **p < 0.01, and ***p < 0.001 (Student’s

t test).

intervention studies. However, further experiments that consider

the complexity of human diets and the variety of dietary fibers are

required to determine how well data from our simplified system

can be translated into human physiology.

STAR+METHODS

Detailed methods are provided in the online version of this paper

and include the following:

d KEY RESOURCES TABLE

d CONTACT FOR REAGENT AND RESOURCE SHARING

d EXPERIMENTAL MODEL AND SUBJECT DETAILS

378

B Colonization of mice with the SIM bacteria

B Colonization of mice with human feces

B Dietary experiments with SIM mice

B Bacteria strains

d METHOD DETAILS

0 Cell Reports 26, 3772–3783, March 26, 2019

B Body composition and glucose tolerance test

B Genomic DNA extraction and 16S rRNA quantitative

PCR

B Quantification of SCFAs and organic acids

B Microbial RNA extraction

B Metatranscriptome analysis of the SIM community

B Metabolomic analysis of plasma from SIM mice

d QUANTIFICATION AND STATISTICAL ANALYSIS

d DATA AND SOFTWARE AVAILABILITY

SUPPLEMENTAL INFORMATION

Supplemental Information can be found with this article online at https://doi.

org/10.1016/j.celrep.2019.02.090.

ACKNOWLEDGMENTS

We are grateful to Carina Arvidsson, Sara Nordin-Larsson, Ulrica Enqvist,

Caroline Wennberg, and Zakarias Gulic for excellent animal husbandry and

the genomics core facility at Sahlgrenska Academy for performing microbiota

RNA sequencing (RNA-seq). We thank Dr. Karen Scott, Dr. Sylvia Duncan, and

Jenny Martin of the Rowett Institute in Nutrition and Health (Aberdeen, UK) for

fruitful discussions during the design of the SIM community and for providing

E. hallii L2-7 (DSM 17630), E. rectale A1-86 (DSM 17629), B. adolescentis L2-

32, C. aerofaciens (DSM 3979), D. piger (DSM 749), R. inulinivorans A2-194

(DSM 16841), and R. bromii L2-63. We thank Professor Willem de Vos and

Dr. Clara Berzel of Wageningen University (the Netherlands) for providing us

with A. muciniphila (DSM 22959). This study was supported by a DuPont

Young Professor Award, the Swedish Research Council, IngaBritt and Arne

Lundberg’s Foundation, JPI (A healthy diet for a healthy life; 2017-01996_3),

the Knut and Alice Wallenberg Foundation, the Novo Nordisk Foundation

(NNF13OC008163), the Leducq Foundation (17CVD01), and a grant from the

Swedish state under the agreement between the Swedish government and

the county councils, the ALF-agreement (ALFGBG-718101). F.B. is a recipient

of a European Research Council (ERC) Consolidator Grant (615362;

METABASE) and is the Torsten Soderberg Professor in Medicine. P.K.-D. is

a recipient of fellowships from the CAS President’s International Fellowship

Initiative (PIFI; project 2018PB0028) and DICP Outstanding Postdoctoral

Foundation (grant 2017YB05). S.S. is a recipient of a fellowship from the

Engineering and Physical Sciences Research Council (EPSRC) (project EP/

S001301/1).

AUTHOR CONTRIBUTIONS

P.K.-D. and F.B. conceived and designed the project. P.K.-D., A.H., and A.W.

collected samples. P.K.-D., S.S, and A.W. performed experiments. I.N. per-

formed statistical and normalization of transcription data. S.L. and S.S. per-

formed the functional analysis of data. P.K.-D., S.S., I.N., A.W., R.P., J.N.,

and F.B. analyzed and interpreted the data. P.K.-D., S.S., R.P., and F.B. wrote

the paper. All authors commented on the manuscript.

DECLARATION OF INTERESTS

J.N. and F.B. are founders and shareholders of Metabogen AB.

Received: November 2, 2018

Revised: January 22, 2019

Accepted: February 21, 2019

Published: March 26, 2019

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Cell Reports 26, 3772–3783, March 26, 2019 3783

STAR+METHODS

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Bacterial and Virus Strains

Akkermansia muciniphila Prof. Willem M. de Vos DSM 22959

Bacteroides thetaiotaomicron ATCC 29148

Bifidobacterium adolescentis Dr. Karen Scott L2-32

Collinsella aerofaciens Dr. Karen Scott DSM 3979

Desulfovibrio piger Dr. Karen Scott DSM 749

Eubacterium hallii Dr. Karen Scott L2-7

Eubacterium rectale Dr. Karen Scott A1-86

Prevotella copri DSM 18205

Roseburia inulinivorans Dr. Karen Scott A2-194

Ruminococcus bromii Dr. Karen Scott L2-63

Chemicals, Peptides, and Recombinant Proteins

Bacto casitone BD Cat#225910

Yeast extract Oxoid Cat#LP0021

Sodium bicarbonate Merck Cat#1.06329.0500

Glucose/microbiology Merck Cat#346351

Cellobiose Sigma-Aldrich Cat#22150

Starch Sigma-Aldrich Cat#S9765

Di-potassium hydrogen phosphate Sigma-Aldrich Cat#P3786

Di-hydrogen potassium phosphate Merck Cat#1.04873.0250

Di-ammonium sulfate Sigma-Aldrich Cat#A4418

Magnesium sulfate heptahydrate Sigma-Aldrich Cat#M7634

Calcium chloride dihydrate Merck Cat#1.02382.0500

Acetic acid Sigma-Aldrich Cat#A6283

Propionic acid Sigma-Aldrich Cat#P1386

n-Valeric acid Sigma-Aldrich Cat#240370

Isovaleric acid Sigma-Aldrich Cat#129542

Isobutyric acid Sigma-Aldrich Cat#I1754

Hemin Sigma-Aldrich Cat#51280

Thiamine-HCl Sigma-Aldrich Cat#T1270

Riboflavin Sigma-Aldrich Cat#R9504

Biotin Sigma-Aldrich Cat#B4639

Cobalamin Sigma-Aldrich Cat#V2876

4-Aminobenzoic acid Sigma-Aldrich Cat#A9878

Folic acid Sigma-Aldrich Cat#F8758

Pyridoxamine dihydrochloride Sigma-Aldrich Cat#P9158

Cysteine Sigma-Aldrich Cat#C7477

Resazurin Sigma-Aldrich Cat#R7017

Rumen fluid Prof. Hauke Smidt N/A

Trypticase peptone BD Cat#211921

Proteose peptone Sigma-Aldrich Cat#82450

Beef extract BD Cat#212303

Tween 80 Sigma-Aldrich Cat#P4780

Vitamin K1 Sigma-Aldrich Cat#V3501

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e1 Cell Reports 26, 3772–3783.e1–e6, March 26, 2019

Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

Sodium sulfide nonahydrate Sigma-Aldrich Cat#208043

Sodium butyrate – 13C4 Sigma-Aldrich Cat#488380

Sodium Acetate �1-13C, d3 Sigma-Aldrich Cat#298042

Propionic acid-d6 Sigma-Aldrich Cat#490644

Succinic acid 13C4 99% Loradan Fine Chemicals AB Cat#CLM-1571

Sodium lactate 13C3 98% Loradan Fine Chemicals AB Cat#CLM-1579

Succinic acid (98%) Loradan Fine Chemicals AB Cat#15-0400

Butyric acid Sigma-Aldrich Cat#19215

Sodium acetate Sigma-Aldrich Cat#S8750

Sodium propionate Sigma-Aldrich Cat#P1880

Sodium DL-lactate Sigma-Aldrich Cat#71720

N-tert-Butyldimethylsilyl-N-

methyltrifluoroacetamide

Sigma-Aldrich Cat#19915

Diethyl ether Sigma-Aldrich Cat#309958

Autoclavable Mouse Breeder Diet 5021 (Chow diet) LabDiet Cat#0006539

Adjusted Fat Diet (HF-HS diet) Envigo TD.09683

62% Sucrose diet (ZF-HS diet) Envigo TD.03314

EDTA Sigma-Aldrich Cat#EDS

TRIS Sigma-Aldrich Cat#76066-1KG

Ammonium acetate Sigma-Aldrich Cat#09688-1KG

Isopropanol Sigma-Aldrich Cat#I9516-500ML

Macaloid Laguna Clay Cat#MBENMAC

DEPC Treated water Sigma-Aldrich Cat#95284

RNase A Solution QIAGEN Cat#158922

DNase I Roche Cat#4716728001

UltraPure Phenol:Water (3.75:1; v/v) Invitrogen Cat#15594-047

UltraPure Phenol:Chloroform:Isoamyl Alcohol

(25:24:1, v/v)

Invitrogen Cat#15593-049

Chloroform:Isoamyl Alcohol (24:1, v/v) Sigma-Aldrich Cat#25666-100ml

Critical Commercial Assays

1x SYBR Green Master Mix Thermo Fisher Scientific Cat#4309155

RNeasy Mini Kit QIAGEN Cat#74106

QIAmp DNA mini Kit QIAGEN Cat#51360

Ribo-Zero Magnetic Kit Nordic Biolabs Cat#MRZB12424

Agilent RNA 6000 Pico Kit Agilent Technologies Cat#5067-1513

ScriptSeq v2 RNA-Seq Library Preparation Kit Nordic Biolabs Cat#SSV21124

FailSafe Enzyme Mix Nordic Biolabs Cat#FSE51100

ScriptSeq Index PCR Primer Nordic Biolabs Cat#SSIP1234

Agencourt AMPure XP Beckman Coulter Cat#A63880

Deposited Data

RNA-Seq data This paper SRA: PRJEB22735

PFAM (Sonnhammer et al., 1997) https://pfam.xfam.org/

dbCAN2 (Zhang et al., 2018) http://cys.bios.niu.edu/dbCAN2/

Akkermansia muciniphila genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_19_collection/

akkermansia_muciniphila_atcc_baa_835

Bacteroides thetaiotaomicron genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_0_collection/

bacteroides_thetaiotaomicron_vpi_5482

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Cell Reports 26, 3772–3783.e1–e6, March 26, 2019 e2

Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

Bifidobacterium adolescentis genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_25_collection/

bifidobacterium_adolescentis_atcc_15703/

Collinsella aerofaciens genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_4_collection/collinsella_

aerofaciens_atcc_25986/

Desulfovibrio piger genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_12_collection/

desulfovibrio_piger_atcc_29098/

Eubacterium hallii genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-

38/bacteria/fasta/bacteria_2_

collection/_eubacterium_hallii_dsm_3353/

Eubacterium rectale genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-

38/bacteria/fasta/bacteria_24_

collection/_eubacterium_rectale_atcc_33656/

Prevotella copri genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_20_collection/

prevotella_copri_dsm_18205/

Roseburia inulinivorans genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_20_collection/

roseburia_inulinivorans_dsm_16841/

Ruminococcus bromii genome build Ensembl bacteria ftp://ftp.ensemblgenomes.org/pub/release-38/

bacteria/fasta/bacteria_21_collection/

ruminococcus_bromii_l2_63/

Experimental Models: Organisms/Strains

Female Swiss Webster Own breeding N/A

Male Swiss Webster Own breeding N/A

Oligonucleotides

Table S5 N/A N/A

Software and Algorithms

HMMER (Eddy, 1998) http://hmmer.org/

STAR (Dobin et al., 2013) https://github.com/alexdobin/STAR

Samtools (Li et al., 2009) http://samtools.sourceforge.net/

HTSeq (Anders et al., 2015) http://htseq.readthedocs.io

edgeR (Robinson et al., 2010) https://bioconductor.org/packages/release/

bioc/html/edgeR.html

Prism (version 6) GraphPad N/A

Other

CFX96 Real-Time PCR Detection System Bio-Rad C1000

Gas Chromatograph-Mass Spectrometer Agilent Technologies 7890A + 5975C series

pH/Ion meter Mettler Toledo S220 SevenCompact

Coy chamber COY Laboratory Products http://coylab.com/products/anaerobic-

chambers/vinyl-anaerobic-chambers/

Glucose meter HemoCue AB HemoCue Glucose 201+

Centrifuge Eppendorf 5430R

Centrifuge Thermo Scientific Heraeus Megafuge 16R. (TX-400 Swinging

Bucket Rotor)

INTELLI-MIXER with rack LabTeamet EL-RM-2L (EL-16mm test tube)

EchoMRI Body Composition Analyzers for Live

Small Animals

Echo Medical Systems http://www.echomri.com

PCR machine Eppendorf Vapo.Protect

Fast-Prep-24 Classic MP Biomedicals Cat#116004500

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Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

Phase Lock Gel, heavy 2.0 mL tube VWR Cat#713-2536

Lysing matrix E Cat#116914100

Zirconia/Silica Beads, 0.1 mm diameter Techtum Lab AB Cat#11079101Z

Glass beads, 3.0 mm diameter VWR Cat#5.1240.03

Screw cap Micro tube 2.0 mL SARSTEDT Cat#72.694.006

PCR plates Low 96-well black Bio-Rad Cat#HSP-9665

Microseal B Adhesive Sealer Bio-Rad Cat#MSB 1001

NanoDrop ND-1000 spectrophotometer Thermo Scientific http://www.nanodrop.com/Products.aspx

Bioanalyzer Agilent Technologies 2100

Freeze dryer LABCONCO Freezone 4.5

Hungate-like tube Ochs Laborbedarf Cat#1020471

CONTACT FOR REAGENT AND RESOURCE SHARING

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Fredrik

Backhed (fredrik.backhed@wlab.gu.se).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Colonization of mice with the SIM bacteriaAll the mouse experiments were performed using protocols approved by the University of Gothenburg Animal Studies Committee.

For all experiments, mice were maintained in plastic gnotobiotic research isolators housed in a climate-controlled room (22�C ±

2�C) and subjected to a 12 h light/dark cycle (7:00 a.m.–7:00 p.m.) with free access to autoclaved water and food. Mice were housed

in groups (n = 4-5 mice/group) according to their dietary treatment.

To colonize mice with the SIM bacteria, adult female GF Swiss Webster mice were orally gavaged twice after a 4 h fast: first, with

0.2 ml of 108 CFU B. thetaiotaomicron to obtain a more reduced gut environment, and three days later with 0.2 ml of mix of 108 CFU

from each of the rest of the SIM strains. Micewere then bredwithmale GF SwissWebster mice to generate SIMmice.Micewere bred

for four generations. To characterize the colonization pattern of SIM bacteria through the gut, intestinal segments, cecal content and

feces were harvested from 8-week-old male SIM mice, immediately snap-frozen in liquid nitrogen and stored at �80�C until further

processed.

Colonization of mice with human fecesTo generate mice colonized with human feces, fecal samples from one woman and one man were separately added to PBS buffer

supplemented with reducing solution (Na2S and cysteine dissolved in NaHCO3 buffer) and transferred by oral gavage to 10-12-week-

old female and male GF Swiss Webster (5 per group), respectively. The mice were colonized for 14 days and fed regular chow diet.

Cecum was harvested, immediately snap-frozen in liquid nitrogen and stored at �80�C until further processed. The selected adult

human donors were healthy, did not have any special dietary requirements, and had not taken any medication in the 4months before

sample donation. The participants gave informed consent and the study was approved by the Ethics Committee at Gothenburg

University.

Dietary experiments with SIM miceAdult male SIMmice aged 12-16 weeks were randomized into 3 groups (5-7mice/group) to receive: chow (5021 rodent diet, LabDiet;

fat 9% wt/wt); HF-HS (fat 20% wt/wt; sucrose 18% wt/wt, 96132 Teklad Custom Research Diet) or ZF-HS (03314 Teklad Custom

Research Diet; sucrose 62%wt/wt) (Table S1). Mice were fed their respective diet ad libitum for 2 weeks and maintained in separate

plastic gnotobiotic research isolators. At the end of the study, bloodwas collected from the portal vein under deep isoflurane-induced

anesthesia following a 4 h fast, and intestinal segments with contents were harvested. All tissues were immediately snap-frozen in

liquid nitrogen and stored at �80�C until further processed.

Bacteria strainsE. hallii L2-7 (DSM 17630), E. rectale A1-86 (DSM 17629), B. adolescentis L2-32, C. aerofaciens DSM 3979, D. piger DSM 749,

R. inulinivorans A2-194 (DSM 16841), and R. bromii L2-63 were obtained from Dr Karen Scott, the Rowett Institute of Nutrition

and Health, Aberdeen, UK. B. thetaiotaomicron ATCC 29148 and P. copri DSM 18205 were obtained from ATCC and DSMZ,

Cell Reports 26, 3772–3783.e1–e6, March 26, 2019 e4

respectively. A. muciniphila DSM 22959 was obtained from Professor Willem de Vos, Laboratory of Microbiology, Wageningen Uni-

versity, the Netherlands. All strains in this study originate from human feces.

E. hallii, E. rectale, B. adolescentis, B. thetaiotaomicron, C. earofaciens, R. inulinivorans were maintained in Hungate-like tube

(Ochs Laborbedarf, Germany) under CO2 in YCFA medium (Ze et al., 2012). R. bromii was maintained in Hungate-like tubes in

M2GSC media supplemented with rumen fluid (Miyazaki et al., 1997). D. piger was maintained in Hungate-like tubes in CO2 in

YCFA medium supplemented with 30 mM lactate. P. copri was grown in PYG medium (Media 104 in the DSMZ catalog), and

A. muciniphila was grown in a basal mucin-based media (Derrien et al., 2004). All bacteria were cultured anaerobically in 15 mL

Hungate tubes at 37�C in a Coy chamber.

METHOD DETAILS

Body composition and glucose tolerance testBody composition (including liver fat) was determined using magnetic resonance imaging (EchoMRI, Houston, TX). For the glucose

tolerance test, mice were fasted for 4 h before intraperitoneal injection of 30% glucose solution (1.5 mg/g body weight). Blood

glucose levels from tail vein were measured at baseline, and after 15, 30, 60, 90 and 120 min.

Genomic DNA extraction and 16S rRNA quantitative PCRAfter isolation of genomic DNA from intestinal segments, cecal content and feces using repeated bead beating (Salonen et al., 2010),

16S rRNA quantitative PCR was performed with a CFX96 Real-Time System (Bio-Rad Laboratories). Samples were analyzed in a

25 mL reaction mix consisting of 12.5 mL 1xSYBR Green Master Mix buffer (Thermo Scientific, Waltham, Massachusetts, USA),

0.2 mM of each primer and 5 mL of template DNA, water or genomic DNA extracted from feces or cecal content. Standard curves

of 16S rRNA PCR product of E. hallii, E. rectale, B. adolescentis, C. aerofaciens, D. piger, R. inulinivorans, R. bromii, B. thetaiotao-

micron, P. copri and A. muciniphila were created using serial 10-fold dilution of the purified PCR product. qPCR was performed as

reported previously: for E. hallii, E. rectale, B. adolescentis, R. inulinivorans, R. bromii and total bacteria (Ramirez-Farias et al., 2009);

for C. aerofaciens (Kageyama et al., 2000); for D. piger (Fite et al., 2004); for P. copri (Matsuki et al., 2002); for B. thetaiotaomicron

(Samuel and Gordon, 2006); and for A. muciniphila (Collado et al., 2007) (Table S5). All reactions were performed in duplicate in

one run and in duplicate PCR runs. The data are expressed as log of 16S rRNA copy per g of luminal content or feces.

Quantification of SCFAs and organic acidsSCFAs and organic acids in the cecumweremeasured by gas chromatography. For the extractions, 100-250mg of frozen cecal con-

tent was transferred to a glass tube (16x125mm) fitted with a screw cap and 100 mL of stock solution of internal standard (1 M [1-13C]

acetate, 0.2 M [2H6]propionate and [13C4]butyrate, 0.5 M [13C]lactate and 40 mM [13C4]succinic acid) was added. Prior to extraction,

samples were freeze-dried at �50 �C for 3 h (yield 40–98 mg dry weight). The extraction and quantification of SCFAs and organic

acids was performed as previously reported (Ryan et al., 2014).

Microbial RNA extractionApproximately 100 mg frozen cecum content was re-suspended in 500 mL ice-cold TE buffer (Tris–HCl pH 7.6, EDTA pH 8.0). Total

RNAwas extracted from the re-suspended cell pellet according to theMacaloid-based RNA isolation protocol (Egert et al., 2007) with

the use of Phase Lock Gel heavy (5 Prime, Hamburg) (Murphy and Hellwig, 1996) during phase separation. The aqueous phase was

purified using the RNAeasy mini kit (QIAGEN, USA), including an on-column DNaseI (Roche, Germany) treatment as described pre-

viously (Egert et al., 2007). Total RNA was eluted in 30 mL ice-cold TE buffer and the RNA quantity and quality were assessed using a

NanoDrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, USA).

Metatranscriptome analysis of the SIM communityThe metatranscriptome datasets were generated by Illumina sequencing of 15 cDNA libraries derived frommRNA enriched samples

of the cecal SIMmicrobiota. ThemRNA enrichment was performed by the removal of 16S and 23S rRNA using sequence-based cap-

ture probes attached to magnetic beads [Ribo-ZeroTM Magnetic Kit (Bacteria), Epicenter, Illumina] using the manufacturer’s proto-

cols. The enriched mRNA was quantified spectrophotometrically (NanoDrop) and its quality was assessed using an Agilent 2100

Bioanalyzer (Agilent Technologies). Thirty Illumina sequencing libraries were constructed from double-stranded cDNA prepared after

RNA fragmentation according to the manufacturer’s protocols (ScriptSeq v2 RNA-Seq Library preparation Kit, Epicenter, Illumina).

Each sequencing library was barcoded (ScriptSeqTM Index PCR Primers, Epicenter, Illumina). Sequencing was performed at the

Genomics Core Facility of the Gothenburg University using Illumina Hiseq2000, which generates on average �15 million reads for

each sample. The reads were trimmed from adaptor sequences and quality (Phred quality score > 30), then simultaneously aligned

to the corresponding bacterial genome of SIM using STAR 2.3.1u (Dobin et al., 2013). The resulting bam-files were indexed and

sorted using Samtools 0.1.18 (Li et al., 2009). Gene counts were calculated using HTSeq-count 0.5.4p3 (Anders et al., 2015) based

on the transcript annotation and globally normalized using a standard negative binomial approach through the R-package edgeR

(Robinson et al., 2010). Circos was used to plot the genome coverage and the comparative analyses of differentially expressed genes

(Krzywinski et al., 2009).

e5 Cell Reports 26, 3772–3783.e1–e6, March 26, 2019

The genes were functionally annotated using KEGG orthology (KO), CAZymes and Pfam. We annotated CAZymes based on the

HMMER, DIAMOND and Hotpep tools of dbCAN2 meta-server (Zhang et al., 2018) using the following: E-value < 1e-15, coverage >

0.35 for HMMER; E-value < 1e-102, hits per query (-k) = 1 for DIAMOND; and frequency > 6.0, hits > 2.6 for Hotpep. We annotated

Pfam domains using HMM profiles of Pfam Database (version 31) by HMMER (default E-value, 10) (Eddy, 1998).

Metabolomic analysis of plasma from SIM miceAliquots of portal vein plasma from SIMmice fed chow, HF-HS or ZF-HS diets were analyzed by untargeted liquid chromatography -

mass spectrometry (LC-MS) and gas chromatography - mass spectrometry (GC-MS) at Metabolon (Durham, USA).

QUANTIFICATION AND STATISTICAL ANALYSIS

Values are presented as means ± SEM. For graph plotting and statistical analysis we used GraphPad Prism (version 6, GraphPad

Software, San Diego, CA, USA) unless otherwise indicated. Statistical comparison of two groups was performed by Student’s

t test, comparisons of three or more groups were performed by one-way analysis of variance (ANOVA) and corrected for multiple

comparison with Tukey post-tests.

DATA AND SOFTWARE AVAILABILITY

The RNA-seq data reported in this paper have been deposited in the ENA sequence read archive (http://www.ebi.ac.uk/ena/data/

view) under accession number PRJEB22735.

Cell Reports 26, 3772–3783.e1–e6, March 26, 2019 e6